Optimal asymptotic quadratic error of density estimators for strong mixing or chaotic data
AbstractUnder mild mixing conditions, we show that the kernel density estimator has exactly the same asymptotic quadratic error as in the i.i.d. case. Curiously, that result remains almost valid if the data are chaotic.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 22 (1995)
Issue (Month): 4 (March)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Vieu, Philippe, 1991. "Quadratic errors for nonparametric estimates under dependence," Journal of Multivariate Analysis, Elsevier, vol. 39(2), pages 324-347, November.
- N. Hosseinioun & H. Doosti & H. Nirumand, 2012. "Nonparametric estimation of the derivatives of a density by the method of wavelet for mixing sequences," Statistical Papers, Springer, vol. 53(1), pages 195-203, February.
- D. Blanke & D. Bosq & D. Guégan, 2003. "Modelization and Nonparametric Estimation for Dynamical Systems with Noise," Statistical Inference for Stochastic Processes, Springer, vol. 6(3), pages 267-290, October.
- Biau, Gérard, 2002. "Optimal asymptotic quadratic errors of density estimators on random fields," Statistics & Probability Letters, Elsevier, vol. 60(3), pages 297-307, December.
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